CC* Compute: Nevada Bridge to AI-enabled Scientific & Engineering Computing (NvBAISEC)

Board Of Regents, Nshe, Obo University Of Nevada, Reno, Reno NV

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The University of Nevada-Reno aims to add a 10 node A100 Graphics Processing Unit (GPU)-based cluster to their pre-existing Central Processing Unit (CPU) cluster with the expressed aim of creating a more central facility to expand access across campus. The projects aims to meet present and anticipated Artificial Intelligence (AI)/Machine Learning (ML) workloads in various research group on campus. Twenty-first century science and engineering is being transformed by the increasing scales of research computing and data. Despite the increase in the demand for large-scale and data-centric computational resources, it is still a struggle to provide domain scientists with the necessary tools and support at campus-levels. Specifically, decentralized computing practices not only bottleneck research because of lack of scale and support, but also decouple computing from higher performance and deeper storage and networks. Accordingly, shifts in institutional cyberinfrastructure strategies are required, with the following guiding priorities: (1) improving user friendly access; (2) removing perceived barriers in the use of scalable infrastructure; and (3) building multidisciplinary communities for next-generation workflows. The University of Nevada, Reno (UNR) will add a new A100 GPU-based cluster to its pre-existing CPU cluster to introduce a new set of paradigms for interdisciplinary computing infrastructure and expand access across campus. Each A100 OnDemand node is equipped with 24-core CPUs and A100 GPU accelerators, interconnected with Infiniband switches to provide effective access to science drivers at UNR and externally through Open Science Grid. This cluster has potential to meet UNR’s present and anticipated workloads of various research groups on campus by addressing three main requirements: (1) capability to support dozens to hundreds of concurrent interactive session users through the Multi Instance GPU (MIG) capabilities of the A100 GPUs; (2) support for modern tensor core architectures to facilitate machine learning workflows; and (3) colocation on the UNR research perimeter network and adjacency to high performance storage. This project is a significant step for the under-resourced institution in computing resources and capability. The planned integration with Open Science Grid (OSG) will open a door to on-demand access to millions of hours of heterogeneous compute cycles for researchers on campus. This project is funded through the collaborative efforts of the Office of Advanced Cyberinfrastructure (OAC) and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.